In recent years, some progress has been made in the key technologies of the architecture and software system for graph processing. Large-scale graph processing has also been widely used in many fields, including scientific computing, machine learning, social networks, intelligent transportation, bioinformatics, etc. However, most real-world graph computations have characteristics such as dynamic changes and complex and diverse application requirements. This poses new demands and challenges for graph processing in terms of basic theory, architecture, and key technologies of system software. To address these challenges, researchers have proposed a series of graph processing systems and accelerators, which optimize the graph processing process through technologies such as high-performance computing and parallel computing. Furthermore, in order to meet the demands of practical application scenarios, various graph processing frameworks and algorithms are constantly being innovated and optimized, thus enhancing the practical value of graph processing in terms of processing large-scale graph data and improving computational efficiency. This paper will review the research and development status of key technologies in graph processing architecture and system software at home and abroad, summarize, compare and analyze the latest progress of research at home and abroad, and select fields closely related to national economy and people’s livelihood in combination with national development strategies and major application requirements. The industry progress of graph processing-related technologies is analyzed and summarized from typical applications. Finally, the future technical challenges and research directions are prospected.